Now that you know how to create graphics and visualizations in R, you are armed with powerful tools for scientific computing and analysis. With this power also comes great responsibility. Effective visualizations is an incredibly important aspect of scientific research and communication. There have been several books (see references) written about these principles. In class today we will be going through several case-studies trying to develop some expertise into making effective visualizations.
The worksheet questions for today are embedded into the class notes.
You can download this Rmd file here
Note, there will be very little coding in-class today, but I’ve given you plenty of exercises in the form of a supplemental worksheet (linked at the bottom of this page) to practice with after class is over.
Fundamentals of Data Visualization by Claus Wilke.
Visualization Analysis and Design by Tamara Munzner.
STAT545.com - Effective Graphics by Jenny Bryan.
ggplot2 book by Hadley Wickam.
Callingbull.org by Carl T. Bergstrom and Jevin West.
Write some notes here about what “effective visualizations” means to you. Think of elements of good graphics and plots that you have seen - what makes them good or bad? Write 3-5 points.
Question: Evaluate the strength of the claim based on the data: “German workers are more motivated and work more hours than workers in other EU nations.”
Very strong, strong, weak, very week, do not know
Very weak, there’s no information about motivation, nor number of people in each country.
Main takeaway: The x axis starts on 37, whereas if it started in 0, the difference between, for instance, France and Germany won’t look as big.
Question: For the years this temperature data is displayed, is there an appreciable increase in temperature?
Yes, No, Do not know
Question: Evaluate the strength of the claim based on the data: ???Soon after this legislation was passed, gun deaths sharply declined."
Very strong, strong, weak, very week, do not know
Very weak, because the y - axis starts on the top.
Main takeaway: The person/persons who made the graphic thought their graph was really cool, but it is misleading and it’s also hard to understand even for someone who knows how to read graphs.
Great resource for selecting the right plot: https://www.data-to-viz.com/ ; encourage you all to consult it when choosing to visualize data.
Correlation different from causation. Both y axis have not the same scale.
Gender Gap in the 100m dash.
We will be filling these principles in together as a class (unfortunately we didn’t get to do this in class, but here are the notes)
Instructions: Below is a code chunk that shows an effective visualization. First, copy this code chunk into a new cell. Then, modify it to purposely make this chart “bad” by breaking the principles of effective visualization above. Your final chart still needs to run/compile and it should still produce a plot.
library("plotly")
library("tidyverse")
ggplot(airquality, aes(`Month`, `Temp`, group = `Month`)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha = 0.3) +
labs(x = "",
y = "",
title="Maximum temperature by month")+
theme_bw() +
scale_x_continuous(breaks=c(5,6,7,8,9),labels=c("May","June","July","August","September")) +
annotate("text", x = 4.08, y = 95,label="??F",size=8) +
coord_cartesian(xlim = c(4.5, 9.5),
clip = 'off')+
theme(panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "gray"),
panel.border = element_blank(),
text = element_text(size=18)
)
How many of the principles did you manage to break?
Did you know that you can make interactive graphs and plots in R using the plotly library? We will show you a demo of what plotly is and why it’s useful, and then you can try converting a static ggplot graph into an interactive plotly graph.
This is a preview of what we’ll be doing in STAT 547 - making dynamic and interactive dashboards using R!
For this demo, make sure you have the following packages installed and loaded:
library(tidyverse)
library(gapminder)
library(plotly)
ggplot2 graphs interactiveIt’s very easy to convert an existing ggplot2 graph into an interactive graph with plotly::ggplotly
On the below graph, explore the interactive options:
p <- gapminder %>%
ggplot(aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point()
p %>%
ggplotly()
plotly::plot_lyWe can also make interactive graphs using the the plotly::plot_ly function:
p <- gapminder %>%
plot_ly(x = ~gdpPercap,
y = ~lifeExp,
color = ~continent,
# mode specifies the geometric object e.g. "markers" for points, "line" for lines
mode = 'markers',
# type controls the "type" of graph e.g. 'bar', 'scatter'
type = 'scatter'
)
p
You are highly encouraged to the cm013 supplemental exercises worksheet. It is a great guide that will take you through Scales, Colours, and Themes in ggplot. There is also a short guided activity showing you how to make a ggplot interactive using plotly.